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Difference Between Data Scientist and Data Analyst

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With emerge of data industries in today’s world, there seems to be no shortage of interesting ideas and opinions about the roles and skillsets that drive this growing field. Have u ever heard of Data science and Data analysis? It seems like same but in reality, both are different. Due to sounding almost same, sometimes it leads to confusion.

Here is the two reasons why both are so confusing:

  • One, different companies have different ways of defining the roles. Job titles are not always an accurate replica of one’s actual job activities and responsibilities.
  • Another one is, data science is a blossoming field and not everyone is familiar with the inner workings of the industry. Now, here have a look at “What distinguishes a Data scientist from a Data analyst?”

Data Scientist: A Data Scientist is a professional who understands data from a business point of view. Data scientists come with a strong groundwork for computer applications, modeling, statistics, and maths. Their brilliance in business coupled with great communication skills apart them from others and they deal with both business and IT leaders. Data scientists are in charge of making predictions to solve and help the businesses take accurate decisions of problems and then it will add value to the organization after resolving it. “Data Scientist” as the “sexiest job of the 21st century” has named by Harvard Business Review.

It can be divided into 4 different categories based on their skill sets.

  • Data Researcher
  • Data Developers
  • Data Creatives
  • Data Businesspeople

Data Analysts: Data Analyst is a part of Data Science. It plays a major role in Data Science. Data Analysts performs various tasks related to collecting, organizing data and obtaining statistical information from them. They are also responsible for presenting the data in the forms of charts, graphs, and tables and then the same data is used for the building of relational database for the organization.

It can also be divided into 4 different categories based on their skill sets.

  • Data Architects
  • Data Administrators
  • Analytics Engineer
  • Operations

How the Data Scientist differs from the Data Analyst?

  • Normally, a data scientist is expected to solve and formulate the questions and then proceed by solving them that will help in business while a data analyst is pursued a solution of given questions by the business team with that guidance.
  • The data scientists have intense humor and strong data visualization skills and ability to convert the data beautifully into a business story. While data analysts are normally not expecting the transformation of data and analysis into a business scenario and roadmap.

Qualification Required For Data Scientists and Data Analysts:


  • They should be familiar with database systems. Example: MySQL, Hive, etc.
  • Should have a clear understanding of various analytical functions–median, rank etc and how to use them on data sets.
  • Knowing ’R” is like a feather on a Data Scientist’s Cap.
  • Perfection in mathematics, statistics, data mining, correlation and predictive analysis better predictions for business decisions.
  • Deep statistical insights and machine learning-Mahout, Bayesian, Clustering etc.


  • Familiar with data warehousing and business intelligence concepts.
  • Data storing and retrieving skills and tools.
  • Strong understanding of Hadoop based analytics(HBase, Hive, MapReduce jobs, cascading etc).
  • proficiency in the decision-making process.
  • In-depth exposure to SQL and analytics.
  • Familiar with various ETL tools-for transforming different sources of data into analytics data stores.

Remember: This is just a sample from a fellow student.

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Difference between Data Scientist and Data Analyst. (2018, December 17). GradesFixer. Retrieved June 25, 2022, from
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